'''
mnist sample
'''

from tensorflow.examples.tutorials.mnist import input_data
# download four files from http://yann.lecun.com/exdb/mnist/
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf 
x = tf.placeholder(tf.float32, [None, 784])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

yy = tf.placeholder(tf.float32, [None, 10])

y = tf.nn.softmax(tf.matmul(x, w) + b)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(yy * tf.log(y), 1))

# y = tf.matmul(x, w) + b
# cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=yy, logits=y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.InteractiveSession()

tf.global_variables_initializer().run()

for _ in range(1000):
	batch_xs, batch_yx = mnist.train.next_batch(100)
	sess.run(train_step, feed_dict={x:batch_xs, yy:batch_yx})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(yy,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(sess.run(accuracy,
	feed_dict={x: mnist.test.images, yy:mnist.test.labels}))
